Does price positively effect customer satisfaction?
Analysis Requirements (Jupyter Notebook)
- Introduce the problem and define key terms
- 5-10 sentences
- At least one credible source for each key term defined
- Answer the business question
- 5-10 sentences
- Make sure your results are statistically significant
- Provide your top two actionable insights
- 5-10 sentences each
- Provide at least one credible source per insight.
- Make sure to go beyond the numbers. Note that the company is likely to already be taking advantage of common metrics such as correlations and is expecting a deeper level of analysis.
- Use markdown to explain the rest of your analysis
- 250-450 words
- Remember that markdown is used to explain what you, the analyst, has found important through the code. Code comments are used to explain the technical aspects of the code.
- Provide the SQL queries needed to:
- explore the data leading up to the creation of your final dataset
- develop your final dataset (this is what will be exported into Excel and then read into Python)
- Make sure to include a USE statement and ample comments throughout your code.
- Do not use AI to generate any of your SQL code.
- Your code must generate the following:
- Descriptive statistics
- Frequency tables
- 3-5 well-designed, highly relevant data visualizations (scatterplots, boxplots, etc.)
- Make sure to avoid data dumping:
- Remove any outputs/visuals that do not directly support your insights
- Limit your tabular outputs
- Do not use AI to generate any of your Python code.
- To get your final dataset from SQL to Python, you may export the data from SQL into an Excel file and then imported into Python with pd.read_excel().
- Avoid writing about what you did. Your stakeholders will assume that you took proper steps to analyze the data and do not have the bandwidth to read through your process. They are more interested in your answer to the business question, as well as your top two actionable insights.
- Note that your stakeholders will start asking questions about the validity of your results if your insights stray from the SQL queries/Python code you provide.
- Additional files (Excel, etc.) will not be assessed.
1. Submit a Jupyter Notebook in the following two formats:
- Jupyter Notebook (.ipynb format)
- HTML page, converted directly from the Jupyter Notebook interface (.html format)
2. Submit your SQL queries in the following two formats:
- SQL script (.sql format)
- Text file (.txt format)